bs_bs_banner Asian Economic Journal 2013, Vol. 27 No. 3, 285 306 285 Evolution of Urban rural Living Standards Inequality in Thailand: 1990 2006* Zheng Fang and Chris Sakellariou Received 21 October 2011; accepted 9 January 2013 The present study examines the developments in urban rural living standard inequality in Thailand from 1990 to 2006 using a methodology that allows for the identification of individual contributors to changes in inequality. We also propose a method to analyze the changes over time in urban rural living standards. We find that the urban rural gap in living standards in Thailand has narrowed substantially since the early 1990s, both at the mean and especially for households in the upper part of the expenditure distribution; however, the narrowing of the gap at the bottom of the expenditure distribution has been negligible. The study also identifies the main individual contributors to changes over time in living standards between urban and rural areas in Thailand. Keywords: unconditional quantile regression, urban rural inequality, Thailand. JEL classification codes: O18, O53. doi: 10.1111/asej.12015 I. Introduction The gap between urban and rural living standards is generally substantial in developing countries. The liberalization of markets over the past three decades and, in particular price liberalization, has resulted in reduced distortions against tradable goods, which tend to be harmful to agriculture (Krueger et al., 1995); one would, therefore, expect a narrowing of urban rural disparities. The offsetting trends in inequality hypothesis (OTI) suggests that, along with rising intrasectoral inequality, there has been an offsetting decline in urban rural inequality. There are, however, other factors that may have had the opposite effect. Such factors might relate to urban residents being more educated and able to better adapt to opportunities emerging from price liberalization. They could also be related to demographic trends in fertility as well as the migration of young and more educated people to urban areas, which leads to improved earning power of urban residents (Eastwood and Lipton, 2003). Finally, another possible factor discussed in the literature is a persistent urban bias relating to political dispositions in developing countries, which results in the rural share of endowments in health infrastructure, education provision and other public spending falling below efficiency and equity optima (Lipton, 1977). *Fang: Division of Economics, Humanities and Social Sciences, Nanyang Technological University, 14 Nanyang Drive, Singapore 637332. Sakellariou (corresponding author): Same address as Fanga. Email: acsake@ntu.edu.sg.
ASIAN ECONOMIC JOURNAL 286 Over the past decade, it has been argued that the rural poor do not benefit due to structural constraints such as unequal land distribution, despite the apparent shift in the urban rural terms of trade; of relevance is also the fact that the poor are not sufficiently engaged in marketing agricultural goods (Jamal and Weeks, 1993; Cornia, 1994; Sahn and Stifel, 2002). Underlying any changes in urban rural inequality are developments over time within urban and rural areas. A number of papers have highlighted the growing concern over poverty and malnutrition in urban areas. It has been argued that in most countries, not only has the absolute number of the urban poor increased over the last two decades, but such increases have been at a rate outpacing corresponding changes in rural areas (Haddad et al. 1999). Similarly, von Braun et al. (1993) suggest that rural urban gaps in living standards could be declining with urban inequality still growing. In the next section we review the published literature on urban rural inequality in Asia and in Thailand in particular and discuss the motivation of this paper. Section III outlines the methodology used to meet our objectives. Section IV discusses the data used and Section V presents the results and discusses their implications. Section VI concludes. II. Literature Review II.1 Urban rural inequality in Asia Eastwood and Lipton (2003) find no global trends in either inter-sectoral or intra-sectoral inequality, while reporting some evidence of uniformity within geographical regions. Thus, while they find evidence consistent with the OTI in Latin America (especially Brazil), they find no support of the OTI in Asia. For the East Asian countries examined, while there is a tendency for intra-sectoral inequality to rise after the 1980s, Eastwood and Lipton do not find any evidence of an offsetting fall in urban rural inequality; rather, the finding is that the urban rural gap is rising in most Asian countries examined. Evidence on China shows that post-1983, with the focus of reforms shifting to the urban sector and wages increasingly tied to productivity, there were significant increases in intra-sectoral and inter-sectoral inequalities (Zhang, 1997). This evidence refutes the OTI for the 1983 1995 period examined. For Indonesia, findings by Akita et al. (1999) for the 1987 1993 period based on a dynamic decomposition of the Theil index reveal a small rise in the urban rural gap, mostly accounted for by demographic changes, accompanied by a small decline in intra-sectoral inequality. Estimates for Malaysia suggest that from 1970 to 1990, inter-sectoral as well as overall inequality declined; developments may have been influenced by the Bhumiputra policy which favoured low-income groups (de Haan and Lipton, 1998). Evidence on the Philippines indicates a U-shaped pattern in urban rural inequality, declining sharply between the mid- 1960s to the mid-1970s and rebounding moderately afterwards. Recent evidence
URBAN RURAL INEUALITY IN THAILAND 287 for Vietnam (Le and Booth, 2010) indicates that urban rural inequality increased over the 1993 1998 period, while it decreased somewhat between 2002 and 2006. II.2 The Thai experience Real per-capita incomes in Thailand tripled from the mid-1970s to the late 1990s. During the same period, income inequality in Thailand also increased significantly (see e.g. Motonishi, 2006). Thailand is regarded as a new industrialized economy and the high growth rates (especially prior to the Asian crisis) have been in the forefront of assessments of Thailand s economic progress. However, there have been less upbeat assessments with regards to income distribution and quality of life, especially in rural areas (e.g. see Siriprachai 1997). Most inequality-related studies for Thailand use data from the 1990s and deal with aggregate income inequality. Examples are Kakwani and Krongkaew (1996, 1997) and Ahuja et al. (1997). Fofack and Zeufack (1999), in contrast, use six waves of socioeconomic survey data from 1986 to 1996 to derive estimates of the scope and trend of the sectoral contribution to overall income inequality in Thailand. One of their findings is that the ratio of the income share of the top decile over the bottom decile, which was almost constant across all sectors up to 1992, increased steadily between 1992 and 1994, irrespective of socioeconomic class. Motonishi (2006) identifies the determinants of income inequality in Thailand (which increased between 1975 and 1998). Results point to the significance of agriculture-related factors. There is also some evidence that sectoral factors, financial development and education level disparities play a roughly equally important role in explaining inequality changes in Thailand. Evidence reviewed in Eastwood and Lipton (2003) indicates that, while urban rural inequality declined from 1975 to 1981, it increased sharply between 1981 and 1992. The limitations of scope in past empirical work on urban rural inequality in Thailand motivate our paper. While past studies have used data from the 1990s and generally address aggregate inequality, we focus on recent years and a longer period (from 1990 to 2006), using a new methodology that: (i) allows the estimation of the components of the urban rural gap and their changes over time at different points of the distribution of the inequality measure used; and (ii) allows the identification of individual contributors to the components of the urban rural gap and its changes over time. Our findings reveal certain positive developments in urban rural inequality in Thailand over the past two decades and the main contributors to these developments. There are two parts in our analysis. First, we use an unconditional quantile regression approach to identify the components and major contributors to urban rural per capita household expenditure inequality. Second, we propose a doubledecomposition method to decompose the changes in the urban rural household expenditure gap into constituent components and identify the major contributors to these inequality changes over time.
ASIAN ECONOMIC JOURNAL 288 III. Methodology III.1 Unconditional quantile regression A recently developed unconditional quantile regression approach (Firpo et al., 2009) is utilized to study the impact of changes in the distribution of covariates on the quantiles of real per capita household expenditure in urban and rural areas. Compared to OLS regression, which only considers the effect at the mean, this approach allows for the examination of changes in the distribution of covariates on household expenditure over the whole expenditure distribution. Furthermore, it provides an easier and more intuitive interpretation of the effects on the marginal distribution of the dependent variable, compared to the traditional quantile regression (Koenker and Bassett, 1978) and allows the derivation of the conditional quantile effect. The estimation of unconditional quantile regression involves two steps. The first step is to derive the recentered influence function (RIF) of the dependent variable; the second step involves estimating an OLS regression of the generated RIF variable on covariates. The influence function (IF), a tool widely used in robust estimation (Hampel, 1974), reflects the influence of an observation on a specific distributional statistic. Adding the statistic back to the influence function generates the RIF function, whose expectation equals precisely the statistic. In the case of quantiles: τ Y τ RIFY ( ; τ, FY)= τ + IFY ( ; τ, FY)= τ + Ι ( ) fy ( τ ) with: E[ RIF( Y;, F )]=, τ Y τ (1) where τ denotes the τth quantile of the outcome distribution F Y. The probability distribution function of variable Y is f Y and I( ) is the indicator function. If the specification of the unconditional quantile regression is linear, that is, RIF ( Y ; τ, FY )= Xβ+ ε, (2) the OLS estimate of β (henceforth, the RIF-OLS estimator), provides a consistent estimate of the marginal effect on the unconditional quantile of a small location shift in the distribution of covariates, X, holding everything else constant. Therefore, E[RIF(Y; τ, F Y) X] = Xβ. Taking the expectation on both sides, the law of iterated expectations and the property of RIF gives: ( F )= E( X) β. (3) τ Y However, if the unconditional quantile regression is not linear, RIF-OLS estimates may not be consistent. Instead, an alternative non-parametric estimator such as RIF-logit or RIF-nonparametric may be used (Firpo et al., 2009). In
URBAN RURAL INEUALITY IN THAILAND 289 addition, it is also noted that when the statistic of interest is the mean, the RIF-OLS estimator coincides with the OLS estimator. III.2 A hybrid approach of quantile decomposition The overall difference in the τth quantile of the outcome between group 0 and 1 can be decomposed as: Δ τ = τ( FY D ) τ( FY D ) 1 = 1 0 = 0 τ τ = ΔS + ΔX, where F Y i D = j represents the distribution of outcome Y i for individuals i in group j. The first part of Equation (1) characterizes the differences in the regression functions, hence defined as the wage structure effect and denoted by Δ τ S ; the second part, which captures the differences in the endowments between individuals in groups 0 and 1, is defined as the composition effect and is denoted by Δ τ X If the regression function is linear, the RIF-OLS estimator is consistent and easy to compute. Furthermore, because τ ( FY D j E X D j β i = )= ( = ) i, the decomposition at quantiles is analogous to the Oaxaca Blinder decomposition at the mean: Δ S (4) τ T = E( X D = 1) ( β β 1 0 ) (5a) τ Δ X = [ E( X D = ) E( X D = )]. T 1 0 β (5b) 0 However, if the specification is not linear, the decomposition based on the linear regression would be biased because the RIF estimate is only a local approximation. 1 Therefore, a hybrid approach using both reweighting (DiNardo et al., 1996) and RIF regression, as suggested by Firpo et al. (2007), is taken to assess the extent of the problem due to the linearity assumption. The two components are estimated from: Δ S τ T = E( X D = 1) ( β β 1 01 ) (6a) 1 In the Firpo et al. (2009) procedure, to estimate the effect of changes in the distribution of X, one uses a first-order linear approximation of the effect; the procedure results in two terms: the first-order linear approximation term and the remaining approximation error. The first-order term is estimated using a mean regression method that exploits the law of iterated expectations and the assumption of linearity of the term. However, the estimate is still an approximation of the effect and the approximation error does not vanish. Chernozhukov et al. (2009) propose an approach that directly estimates the exact effect without an approximation error. Rothe (2010) shows that the effect of counterfactual changes (both fixed and infinitesimal) in the unconditional distribution of a single covariate on the unconditional distribution of an outcome variable of interest are point identified only if the covariate affected is continuously distributed; in contrast, if the distribution of the covariate is discreet, the effects are only partially identified. He then derives informative bounds for these effects.
ASIAN ECONOMIC JOURNAL 290 τ Δ X T [ ( ) ( = )] β + = E X D = 1 E X D 0 0 R, (6b) where β 01 is the coefficient from estimating the RIF regression in the sample D = 0, reweighted to have the same distribution of covariates as in sample D = 1. Hence, Δ τ S is purged of the influence of different distributions of covariates and reflects only the effect from different wage structures. The approximation error R in Δ τ X is calculated as the difference between the composition effect estimated from the reweighting approach and that from the RIF regression. The smaller R is, the better the linear specification. III.3 Decomposition of changes over time To study the determinants of the change in urban rural living standards inequality over time at the τth quantile, a double decomposition approach based on the hybrid quantile decomposition in Section III.2 is proposed. From Equation (4), the change over time of the outcome variable between two groups in period 1 and period 0 at the τth quantile can be decomposed into the change in composition effects over time, DΔ τ X, and the change in wage structure effects over time, DΔ τ S : Δ Δ = [ ΔS + ΔX ] ΔS + Δ τ τ = DΔ + DΔ. τ τ τ τ τ τ 1 0 1 1 0 X0 X S [ ] (7) Furthermore, each component can be divided into the contribution of each covariate in a similar way as in the hybrid quantile decomposition. In the case of decomposing the change in urban rural living standards inequality between period 1 and period 0, following Equations (4) and (7), the detailed double decomposition can be written as: [ ] + D T ΔX τ = ( X 1 u X 1 r) ( X 0 u X 0 r) β 1 r U, (8a) T DΔ τ = ( X 1 ) [( β 1 β 1 ) ( β 0 β 0 )]+ V, (8b) S u u c u c where, U and V measure how the characteristics (coefficients) component in period 0 would have changed if it was priced as in rural (urban) areas in period 1. They are reported as other in Table 6. 2 t The coefficients β D ( D= u, r; t = 0, 1) are estimated from the unconditional quantile regression, that is, from estimating the RIF regression for τth quantile of the dependent variable (logarithm of the real per capita expenditure), on the t covariates for group D in period t. The counterfactual coefficients β c ( t = 01, ) are ( ) T 0 0 1 0 2 The expressions for the terms U and V are given below: U = Xu Xr ( βr β r )+ R1 R0, T 1 0 0 0 V = X X ( β β ). ( ) u u u c
URBAN RURAL INEUALITY IN THAILAND 291 obtained from reweighing, such that the rural sample in period t is reweighted to have the same distribution of covariates as the urban sample in the same period. t XD ( D= u, r; t = 01, ) is the vector of the means of explanatory variables in group D, period t, while R t (t = 0,1) is the approximation error in the composition effect in period t. The detailed double decomposition, therefore, can be carried out and has an intuitive interpretation as well. For instance, we are now able to see, at any quantile of interest, by how much any narrowing of the urban rural education gap has contributed to the inequality change and how the compositional differences in industry has evolved to influence the overall living standards gap between urban and rural households. IV. Data Although data enabling the study of issues related to changes in inequality is scarce, Thailand s Socioeconomic Survey of Households (HSES) is an exception. One advantage of the HSES is that it provides a variety of information for each household, including household income and consumption, representative at the changwat (province) level. Household surveys, the best available data source for household income and consumption, are generally only representative at the province level. In Thailand, for example, the changwat is the stratum in the Socio-Economic Survey (SES) and the lowest attainable geographic level of disaggregation for SES-based poverty and inequality measures (Healy and Jitchushon, 2003). The HSES has been conducted by the National Statistical Office (NSO) of Thailand since 1968, approximately every 5 years before 1987 and every 2 years thereafter. The objective is to collect data on income, expenditure and other characteristics of households. The data cover five regions (Greater Bangkok Metropolitan Area, Central Region, Northern Region, North-eastern Region, and Southern Region) and of these five regions, four have three sub-regions (municipal areas, sanitary districts and villages), while the Greater Bangkok Metropolitan Area has no subdivisions. After the year 2000, all sanitary districts were upgraded to municipalities. In this paper, an urban area is defined as a municipal or sanitary district for data-years 1990 and 1998, while for 2006 an urban area is defined as a municipal area only; for all data-years, rural refers to villages. In this way, we minimize the effect of the change in administrative units on the measure of the urban rural disparity. The measure of inequality in this study is monthly real per-capita household consumption expenditure, derived using available information on monthly household consumption expenditure, the number of members of the household and the regional consumer price index (CPI) for urban and rural areas, respectively. 3 Using consumption as the measure of inequality is the preferred approach 3 If it was available, using provincial level CPI for urban and rural areas would have been preferable. Regional CPI data can be acquired from authors upon request.
ASIAN ECONOMIC JOURNAL 292 compared to using income, at least for developing countries; the main reason is that it is a more direct measure of household wellbeing, as income is subject to transitory fluctuations (for further discussion see Deaton (1997) and Van de Walle and Gunewardena (2001)). Data for 3 years were used: 1990, 1998 and 2006. 4 For the purpose of comparability, monthly expenditure has been truncated at 20 000 baht; as a result, for 1990, the top 0.1 percent of the pooled data is excluded, for 1998, the top 0.25 percent, and for 2006, the top 1 percent. From Figure A1 (nominal per-capita consumption by year), the proportion of households whose monthly per capita expenditure exceeded 5000 baht increased substantially in 2006, partially as a result of substantial inflation that year. We also observe that rural households at the top 10 percent of the expenditure distribution are only slightly better off compared to the median urban household, and that the proportion of urban households that have the same living standards as those rural households that are at the 90th percentile of expenditure was approximately 60 percent in 1990, 68 percent in 1998 and 75 percent in 2006. Figure A2 shows the kernel density distributions for the logarithm of urban and rural real monthly expenditure in years 1990, 1998 and 2006. The urban distribution lies to the right of the rural distribution in all 3 years, and both distributions shift to the right gradually over time, highlighting that while urban living standards are consistently higher than rural living standards, there has been some convergence over time. We also observe that the urban distribution is more dispersed compared to the rural distribution, especially in the 1990s, which suggests higher inequality within urban compared to rural households. At the same time, the within-group inequality in urban and rural households seems to be converging. Comparing conventional measures of inequality (Gini and Theil indices) by year and within urban and rural areas we find that, overall as well as within urban and rural areas, inequality declined from 1990 to 1998; in the following years, however, there is evidence of an increase in inequality in living standards, especially in rural areas, with both inequality indices for within areas being higher in 2006 compared to 1990. 5 V. Results V.1 Model specification and variables We model household per capita expenditure, the measure of living standards, as a function of demographic characteristics, education, labor force participation and other characteristics of households (Table 1). Two sets of characteristics are utilized: one includes education, employment status and other characteristics of 4 Approximately 25 000 households were surveyed in 1990, 24 000 in 1998 and 45 000 in 2006. 5 Calculations are available from the authors upon request.
URBAN RURAL INEUALITY IN THAILAND 293 Table 1 Definition of variables Variable Characteristics of household head hhyrseduc hhexp hhexp2/100 hhmale hhmarried hhind1 hhind2 hhind3 hhind4 hhind5 hhind6 hhind7 hhind8 hhind9 Characteristics of household hn15 hn60 hsize hearn central north south northeast Definition Year of education of household head Years of labor market experience of household head Square of years of experience of household head/100 Male household head Household head married Household head: Agriculture, hunting, forestry and fishery Household head: Mining and quarrying Household head: Manufacturing Household head: Electricity, gas and water supply Household head: Construction Household head: Wholesale and retail Household head: Hotels, financial intermediation, real estate, public administration/defense, education, health and social services Household head: Transport, storage and communications Household head: Economically inactive Household members under the age of 15 years Household members over the age of 60 years Number of household members Number of earners in the household Region: Central Region: North Region: South Region: North-east the head of household; and the other mainly demographic characteristics of the household. The set of explanatory variables used in our specification is broadly similar to those used in Nguyen et al. (2007) and Le and Booth (2010) in their studies on Vietnam. The dependent variable is the logarithm of real monthly per-capita household consumption expenditure, derived from dividing total household consumption expenditure deflated using the regional CPI for urban and rural areas by the number of members of the household. Table 1 gives the variable definitions, Table 2 presents real per capita expenditure by year and area at different percentiles. Real per capita expenditure increased over time in both urban and rural areas, at the mean as well as at all points of the expenditure distribution. Furthermore, the ratio of urban to rural expenditure increased at percentiles within every year, but decreased over time at any percentile; this is indicative of urban rural living standards converging over time. Figure A3 shows the extent and trend of urban rural inequality in per capita expenditure over time more clearly. The model covariates include the household head s gender, years of education, years of experience and its square, marital status and industry affiliation, and the
ASIAN ECONOMIC JOURNAL 294 Table 2 Real household expenditure per capita (2007 prices) 1990 1998 2006 Urban Rural Ratio Urban Rural Ratio Urban Rural Ratio 10th 1297.79 745.50 1.74 1574.44 984.58 1.60 1903.79 1161.19 1.64 20th 1778.60 918.92 1.94 2117.72 1183.83 1.79 2532.44 1446.83 1.75 30th 2222.43 1068.14 2.08 2639.41 1377.89 1.92 3122.32 1724.28 1.81 40th 2628.68 1204.96 2.18 3148.73 1584.83 1.99 3712.97 2000.00 1.86 50th 3137.07 1380.63 2.27 3702.34 1810.95 2.04 4382.26 2322.02 1.89 60th 3723.25 1595.19 2.33 4384.52 2088.69 2.10 5214.07 2716.77 1.92 70th 4380.52 1844.60 2.37 5275.70 2460.30 2.14 6188.97 3238.68 1.91 80th 5522.06 2259.01 2.44 6471.58 3000.00 2.16 7851.89 4015.34 1.96 90th 7500 3105.86 2.41 8889.72 4074.44 2.18 11265.10 5643.00 2.00 Mean 4200.06 1823.41 2.30 4772.36 2318.50 2.06 5872.92 3136.02 1.87 Observations 29 476 23 091 44 597 41 216 88 262 57 866 following characteristics of the household: household size, household composition (number of members under age 15 and number of members over age 60), number of household earners and region of location of household. Four regional dummies were used to control for five regional differences, which are expected to account for a significant part of overall inequality. V.2 Decomposition results by year The RIF-OLS regression coefficient estimates derived for both urban and rural areas for each year (suppressed here for the interest of conserving space, but available upon request), show that there is a negative relationship between the number of children age 15 and under as well as the number of members 60 years or older and per capita consumption; the smaller the household size, the higher per capita expenditure is, with the exception of rural households in 1990. Surprisingly, in the 1990s, more earners within a rural household are associated with lower monthly per capita consumption. However, by 2006, more earners are associated with better living standards of the household, as one would expect. With the North region as reference, living standards were higher in the Central and South region and lower in the North-eastern region. The sign of the coefficient of the sex of the head of household suggests that in Thailand female-headed households are associated with better living standards, while the effect of marital status of the household head on living standards is small and mixed. As expected, the more educated the household head, the higher the per capita consumption of household members. Furthermore, the return to education is increasing across the distribution. In 1990, the return to education increases from 0.04 at the 10th percentile to 0.09 at the 90th percentile in urban households and from 0.04 to 0.15 in rural households; in later years, the positive effect of education on living
URBAN RURAL INEUALITY IN THAILAND 295 Table 3 Decomposition results, 1990 10th 25th 50th 75th 90th Mean Gap 0.481*** 0.611*** 0.739*** 0.788*** 0.819*** 0.772*** Characteristics 0.295*** 0.383*** 0.484*** 0.576*** 0.475*** 0.536*** Coefficients 0.186*** 0.228*** 0.256*** 0.212*** 0.344*** 0.236*** Contributors to characteristics component hn15 0.060*** 0.059*** 0.068*** 0.076*** 0.063*** 0.064*** hn60 0.004*** 0.003*** 0.003*** 0.005*** 0.007*** 0.004*** hsize 0.003 0.001 0.002 0.009*** 0.003 0.002 hearn 0.020*** 0.028*** 0.038*** 0.057*** 0.052*** 0.038*** hregion 0.089*** 0.097*** 0.200*** 0.234*** 0.245*** 0.168*** hhhsex 0.003 0.004** 0.002 0.015*** 0.021*** 0.006*** hhmarrital 0.007*** 0.004** 0.001 0.010*** 0.011*** 0.002 hhedu 0.112*** 0.093*** 0.178*** 0.235*** 0.434*** 0.216*** hhexp 0.033*** 0.030***.054*** 0.051*** 0.084*** 0.045*** hhind 0.018 0.018** 0.049*** 0.153*** 0.231*** 0.089*** Residual 0.055*** 0.115*** 0.003 0.131*** 0.478*** Contributors to coefficients component hn15 0.016 0.144*** 0.060* 0.015 0.041 0.057*** hn60 0.006 0.005 0.014 0.014 0.011 0.007* hsize 0.129 0.508*** 0.683*** 0.332*** 0.045 0.258*** hearn 0.036 0.212*** 0.347*** 0.210*** 0.093 0.253*** hregion 0.416*** 0.187*** 0.136*** 0.048 0.054 0.068*** hhhsex 0.011 0.037 0.054 0.105 0.095 0.015 hhmarrital 0.059 0.049 0.134** 0.084 0.085 0.033* hhedu 0.176** 0.105 0.070 0.126** 0.104* 0.025 hhexp 0.018 0.089** 0.007 0.258*** 0.110 0.060* hhind 0.077 0.148** 0.143** 0.097 0.442** 0.077** Constant 0.090 0.257** 0.242** 0.745*** 0.168 0.045 Note: ***, **, * = significant at the 1%, 5%, and 10% levels, respectively. standards is more obvious in urban areas, while in rural areas the effect seems to remain unchanged. The more experienced the household head, the higher the per capita expenditure; this effect is larger at the top of the distribution relative to the bottom in urban areas. Tables 3 5 reports the decomposition results at quantiles for years 1990, 1998 and 2006 using the hybrid approach. In parentheses are the bootstrap errors with 100 replications. The decomposition results at the mean (Oaxaca Blinder decomposition) are also listed in the last column. In 1990, the mean gap was 77.2 percent, while at the 10th, 25th, 50th, 75th and 90th percentiles, it amounted to 48.1, 61.1, 73.9, 78.8 and 81.9 percent, respectively, thus monotonically increasing along the distribution. Between 1998 and 2006, the urban rural living standards gap exhibits a similar pattern, with the differential decreasing over time, except at the bottom of the distribution. Considering the results of the overall decomposition and looking at the first three rows of each sub-table, we find that for all 3 years, more than 60 percent of
ASIAN ECONOMIC JOURNAL 296 Table 4 Decomposition results, 1998 10th 25th 50th 75th 90th Mean Gap 0.453*** 0.605*** 0.689*** 0.741*** 0.757*** 0.673*** Characteristics 0.284*** 0.379*** 0.471*** 0.506*** 0.530*** 0.454*** Coefficients 0.169*** 0.226*** 0.217*** 0.235*** 0.226*** 0.219*** Contributors to characteristics component hn15 0.039*** 0.045*** 0.044*** 0.043*** 0.046*** 0.041*** hn60 0.004*** 0.005*** 0.006*** 0.006*** 0.002** 0.005*** hsize 0.009*** 0.007*** 0.008*** 0.011*** 0.010*** 0.011*** hearn 0.006*** 0.007*** 0.004** 0.000 0.001 0.004*** hregion 0.081*** 0.109*** 0.149*** 0.190*** 0.180*** 0.141*** hhsex 0.003*** 0.004*** 0.004*** 0.003*** 0.005** 0.004*** hhmarrital 0.004*** 0.003*** 0.001 0.001 0.005** 0.001* hhedu 0.074*** 0.093*** 0.138*** 0.222*** 0.384*** 0.188*** hhexp 0.016*** 0.019*** 0.033*** 0.043*** 0.058*** 0.036*** hhind 0.048*** 0.059*** 0.102*** 0.151*** 0.146*** 0.098*** Residual 0.041*** 0.074*** 0.049*** 0.077*** 0.191*** Contributors to coefficients component hn15 0.021 0.009 0.012 0.078*** 0.013 0.008 hn60 0.014* 0.006 0.012* 0.003 0.022 0.005 hsize 0.047 0.084*** 0.018 0.069 0.128 0.039* hearn 0.027 0.037 0.018 0.042 0.003 0.068*** hregion 0.243*** 0.299*** 0.185*** 0.131** 0.013 0.122*** hhsex 0.032* 0.038** 0.045*** 0.007 0.034 0.025** hhmarrital 0.004 0.000 0.048** 0.024 0.040 0.005 hhedu 0.064 0.086** 0.031 0.043 0.062 0.028** hhexp 0.043*** 0.019 0.003 0.042 0.109 0.007 hhind 0.032 0.065** 0.036 0.076 0.302 0.039** Constant 0.197*** 0.099* 0.065 0.037 0.207 0.016 Note: ***, **, * = significant at the 1%, 5%, and 10% levels, respectively. the urban rural expenditure gap can be explained by differences in characteristics between urban and rural households. The remainder is due to the urban rural differences in returns to those characteristics (coefficients structure). Within the composition effect, the most important contributor to the urban rural gap is education. For instance, in 1990, if the average years of education of the household head were the same in urban and rural areas, the inequality in urban rural living standards would have been reduced by 21.6 percent on average, which varies from 11.2 percent at the bottom to 43.4 percent at the top of the distribution. The impact of urban rural differences in education attainment on inequality was relatively less significant in later years. In 1998, it accounted for 18.8 percent of the inequality on average, ranging from 7.4 percent at the 10th percentile to 38.4 percent at the 90th percentile, while in 2006, the contribution of the education gap is 19 percent on average, varying from 10.5 to 39.1 percent. The other two characteristics that are important in explaining urban rural inequality are region of residence and industry affiliation of the household head. In 1990,
URBAN RURAL INEUALITY IN THAILAND 297 Table 5 Decomposition results, 2006 10th 25th 50th 75th 90th Mean Gap 0.481*** 0.569*** 0.623*** 0.644*** 0.679*** 0.607*** Characteristics 0.321*** 0.395*** 0.435*** 0.466*** 0.494*** 0.424*** Coefficients 0.160*** 0.174*** 0.188*** 0.178*** 0.186*** 0.184*** Contributors to characteristics component hn15 0.045*** 0.042*** 0.042*** 0.040*** 0.031*** 0.040*** hn60 0.006*** 0.006*** 0.006*** 0.006*** 0.005*** 0.006*** hsize 0.006*** 0.006*** 0.004*** 0.007*** 0.012*** 0.006*** hearn 0.002** 0.003*** 0.003*** 0.006*** 0.008*** 0.003*** hregion 0.085*** 0.119*** 0.148*** 0.140*** 0.119*** 0.121*** hhsex 0.005*** 0.002*** 0.001*** 0.000 0.000 0.001*** hhmarrital 0.001 0.001*** 0.001*** 0.004*** 0.004*** 0.002*** hhedu 0.114*** 0.105*** 0.139*** 0.224*** 0.391*** 0.190*** hhexp 0.033*** 0.028*** 0.026*** 0.030*** 0.052*** 0.035*** hhind 0.034*** 0.074*** 0.112*** 0.121*** 0.142*** 0.096*** Residual 0.062*** 0.072*** 0.010 0.039*** 0.150*** Contributors to coefficients component hn15 0.008 0.016 0.067*** 0.022 0.008 0.002 hn60 0.028** 0.001 0.002 0.008 0.010 0.000 hsize 0.002 0.131*** 0.200*** 0.047 0.229*** 0.041** hearn 0.049** 0.004 0.052** 0.095*** 0.033 0.034*** hregion 0.056*** 0.108*** 0.081*** 0.068*** 0.099*** 0.010 hhsex 0.019* 0.009 0.011 0.017 0.088* 0.003 hhmarrital 0.023 0.020 0.018 0.046* 0.128*** 0.007 hhedu 0.125*** 0.013 0.082*** 0.098*** 0.039 0.032* hhexp 0.080*** 0.118*** 0.063*** 0.008 0.045 0.055*** hhind 0.082*** 0.030 0.155*** 0.202*** 0.152 0.075*** Constant 0.026 0.033 0.048 0.061 0.135 0.121*** Note: ***, **, * = significant at the 1%, 5%, and 10% levels, respectively. urban rural differences in regional composition explained 8.9 percent of the overall gap at the 10th percentile, increasing along the distribution to 24.5 percent at the 90th percentile; in 2006, the contribution of regional differences across the distribution exhibits an inverse U-shape, which reaches the maximum value of 14.8 percent at the median. The contribution of the number of elderly members, household size, sex and marital status of the household head are marginal. However, the urban rural household difference in the number of children under age 15 matters, and accounts for approximately 10 percent of the inequality in 1990, 6 percent in 1998 and 7 percent in 2006. This result is consistent with the fact that rural households have more children compared to urban households and that having more children has a negative effect on family living standards, because on the one hand children do not contribute to family income and on the other, there are considerable costs associated with raising a child. Next, consider the detailed decomposition of the coefficients effect (expenditure structure). It seems that within this effect, the key factor contributing to the
ASIAN ECONOMIC JOURNAL 298 overall inequality is region of residence. For example, in 1990 and 1998, the urban rural living standards inequality that would have prevailed if the effects of region were the same between urban and rural areas is decreasing along the distribution, from a significant 41.6 percent at the 10th percentile to an insignificant 5.4 percent at the 90th percentile in 1990, and from a significant 24.3 percent to a negative but insignificant 1.3 percent in 1998. In 2006, the region premium is significant over the whole distribution. Regarding the contribution of other variables in the structure effect, the results are mixed and irregular. V.3 Decomposing changes over time Table 6 reports the decomposition changes in inequality over time. First, consider panel (a). Compared to 1990, inequality in urban rural living standards in year 2006 has declined by 16 percent on average, of which 11 percent is due to a convergence in characteristics. Across the expenditure distribution, the convergence varies from 3.6 percent at the 25th percentile to 12.9 percent at the 90th percentile. Looking at the double decomposition results, changes in the urban rural characteristics gap contributed approximately 77 percent towards convergence at the 75th percentile, while changes in urban rural differences in coefficients account for more than 58 percent of the inequality change at other quantiles. The results in the detailed double decomposition show that, with respect to changes in the characteristics effect, the main contributors to the decrease in urban rural inequality are region, industry affiliation of the head of household, education attainment of the head of household and number of children in the household. In particular, changes in the regional distribution of households are associated with a 6.7-percent decline in inequality at the bottom, 11.9 percent at the median and 9.6 percent at the top of the expenditure distribution. The contribution of changes in the education qualifications of heads of household in reducing inequality is monotonically increasing across the distribution, from 1 percent at the bottom to 3 percent at the top. Changes in industry affiliation of the household head explain 4.3 percent of the inequality change on average, ranging from 2.8 percent at the 10th percentile to 5.9 percent at the 90th percentile. The impact of changes in the number of household members younger than 15 years is relatively constant across the distribution, explaining approximately 1.7 percent of the inequality convergence. Finally, the last row (component U, referred to as other ) in the second panel ( DΔ τ X ) indicates that the characteristics effect in year 1990 would have been significantly higher if it was priced using the 2006 rural coefficients structure. Next, consider the second part (panel 3) of the detailed double decomposition. With reference to the mean, the effect of household size and number of earners are the most important factors accounting for inequality change; however, they nearly offset each other. The other influential factors are the return to education attainment and industry affiliation, and they also seem to offset each other. Decomposition at the mean conceals the various effects at different parts of the
URBAN RURAL INEUALITY IN THAILAND 299 Table 6 Decomposition of changes over time (double decomposition) a. 2006 1990 10 25 50 75 90 Mean Change in gap 0.0048 (0.017) 0.0362** (0.017) 0.1243*** (0.020) 0.1381*** (0.024) 0.1292*** (0.037) 0.1644*** (0.010) DΔ t X 0.0246 (0.015) 0.0146 (0.016) 0.0496*** (0.015) 0.1090*** (0.032) 0.0180 (0.034) 0.1110*** (0.014) DΔ t S 0.0294* (0.017) 0.0507** (0.022) 0.0747*** (0.024) 0.0291 (0.037) 0.1472** (0.060) 0.0533*** (0.013) DΔ t X (due to change in urban rural differences in characteristics) hn15 0.0198*** 0.0184*** 0.0186*** 0.0175*** 0.0138*** 0.0174*** (0.003) (0.003) (0.003) (0.003) (0.002) (0.003) hn60 0.0017* 0.0017* 0.0015 0.0015 0.0012 0.0016 (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) hsize 0.0030 0.0028*** 0.0023** 0.0031 0.0057* 0.0032*** (0.002) (0.001) (0.001) (0.002) (0.003) (0.001) hearn 0.0109*** 0.0138*** 0.0160*** 0.0278*** 0.0380*** 0.0160*** (0.004) (0.003) (0.003) (0.004) (0.006) (0.002) hregion 0.0666*** 0.0962*** 0.1185*** 0.1123*** 0.0962*** 0.0956*** (0.003) (0.003) (0.004) (0.004) (0.005) (0.003) hhmale 0.0042*** 0.0020** 0.0006*** 0.0003 0.0002 0.0013*** (0.001) (0.001) (0.000) (0.001) (0.001) (0.000) hhmarried 0.0004*** 0.0004*** 0.0006*** 0.0019* 0.0020** 0.0008*** (0.000) (0.000) (0.000) (0.001) (0.001) (0.000) hhedu 0.0098*** 0.0086*** 0.0110*** 0.0176*** 0.0301*** 0.0152*** (0.003) (0.003) (0.003) (0.005) (0.011) (0.004) hhexp 0.0103*** 0.0101*** 0.0093*** 0.0074*** 0.0115*** 0.0105*** (0.002) (0.002) (0.001) (0.002) (0.003) (0.002) hhind 0.0283*** 0.0468*** 0.0489*** 0.0302*** 0.0587*** 0.0425*** (0.004) (0.003) (0.004) (0.005) (0.011) (0.003) other 0.1330*** 0.1640*** 0.1241*** 0.0368 0.1742*** 0.0369*** (0.017) (0.016) (0.016) (0.031) (0.035) (0.013) DΔ t S (due to change in urban rural differences in coefficients) hn15 0.0046 0.0976*** 0.0378 0.0287 0.0154 0.0430*** (0.031) (0.031) (0.026) (0.033) (0.051) (0.011) hn60 0.0369** 0.0063 0.0186 0.0210 0.0340 0.0093 (0.018) (0.015) (0.015) (0.023) (0.034) (0.006) hsize 0.1032 0.3033*** 0.3401*** 0.3208*** 0.0838 0.2561*** (0.085) (0.088) (0.090) (0.124) (0.222) (0.037) hearn 0.0719 0.1840*** 0.2399*** 0.2736*** 0.0736 0.2558*** (0.055) (0.051) (0.049) (0.078) (0.132) (0.021) hregion 0.3059*** 0.0203 0.0397 0.0209 0.0939 0.0346** (0.046) (0.037) (0.036) (0.045) (0.081) (0.015) hhmale 0.0335 0.0477 0.0502 0.0720 0.1569 0.0166 (0.033) (0.050) (0.041) (0.080) (0.117) (0.023) hhmarried 0.0340 0.0625 0.1363*** 0.0185 0.0521 0.0236 (0.044) (0.056) (0.047) (0.085) (0.138) (0.028) hhedu 0.0852** 0.2517*** 0.0021 0.2833* 0.0472 0.1066*** (0.039) (0.049) (0.081) (0.158) (0.253) (0.024)
ASIAN ECONOMIC JOURNAL 300 Table 6 (continued) a. 2006 1990 10 25 50 75 90 Mean hhexp 0.1799*** 0.1240* 0.0085 0.2447 0.2591 0.0261 (0.067) (0.067) (0.087) (0.149) (0.259) (0.044) hhind 0.3782*** 0.1650** 0.1526** 0.0238 0.0863 0.1155*** (0.083) (0.073) (0.068) (0.076) (0.100) (0.037) const 0.1350 0.2722** 0.1210 0.6158*** 0.0352 0.0727 (0.125) (0.118) (0.141) (0.238) (0.337) (0.059) other 0.1313*** 0.0938*** 0.0224 0.0294 0.0515 0.0447*** (0.022) (0.022) (0.024) (0.036) (0.070) (0.012) b. 2006 1998 10 25 50 75 90 Mean Change in gap 0.0283*** (0.010) 0.0357*** (0.011) 0.0662*** (0.010) 0.0963*** (0.014) 0.0685*** (0.020) 0.0666*** (0.008) DΔ X 0.0356*** (0.009) 0.0139 (0.011) 0.0357*** (0.010) 0.0366*** (0.013) 0.0320 (0.029) 0.0308*** (0.006) DΔ S 0.0073 (0.011) 0.0496*** (0.011) 0.0305*** (0.011) 0.0598*** (0.014) 0.0365 (0.031) 0.0359*** (0.008) DΔ t X (due to change in urban rural differences in characteristics) hn15 0.0036* 0.0029 0.0030 0.0028 0.0022** 0.0030 (0.002) (0.002) (0.002) (0.002) (0.001) (0.002) hn60 0.0009 0.0010 0.0009 0.0009 0.0007*** 0.0009 (0.001) (0.001) (0.001) (0.001) (0.000) (0.001) hsize 0.0096*** 0.0085*** 0.0064*** 0.0095*** 0.0172*** 0.0093*** (0.002) (0.001) (0.001) (0.002) (0.003) (0.001) hearn 0.0047** 0.0059*** 0.0069*** 0.0119*** 0.0162*** 0.0068*** (0.002) (0.001) (0.001) (0.002) (0.003) (0.001) hregion 0.0058*** 0.0095*** 0.0120*** 0.0118*** 0.0108*** 0.0099*** (0.001) (0.002) (0.002) (0.002) (0.002) (0.002) hhmale 0.0005 0.0002*** 0.0001*** 0.0000 0.0000 0.0001*** (0.001) (0.000) (0.000) (0.000) (0.000) (0.000) hhmarried 0.0002*** 0.0001*** 0.0002*** 0.0008*** 0.0008 0.0003*** (0.000) (0.000) (0.000) (0.000) (0.001) (0.000) hhedu 0.0138*** 0.0125*** 0.0164*** 0.0268*** 0.0462*** 0.0227*** (0.002) (0.002) (0.003) (0.005) (0.008) (0.003) hhexp 0.0072*** 0.0075*** 0.0067*** 0.0051*** 0.0079*** 0.0071*** (0.001) (0.001) (0.001) (0.001) (0.002) (0.001) hhind 0.0119*** 0.0171*** 0.0116*** 0.0044 0.0077 0.0093*** (0.003) (0.002) (0.003) (0.004) (0.007) (0.002) other 0.0697*** 0.0522*** 0.0013 0.0054 0.0295 0.0108* (0.010) (0.010) (0.010) (0.014) (0.032) (0.006) DΔ t S (due to change in urban rural differences in coefficients) hn15 0.0097 0.0096 0.0592*** 0.0256 0.0234 0.0092 (0.019) (0.016) (0.017) (0.026) (0.048) (0.009)
URBAN RURAL INEUALITY IN THAILAND 301 Table 6 (continued) b. 2006 1998 10 25 50 75 90 Mean hn60 0.0457*** 0.0061 0.0155 0.0022 0.0128 0.0058 (0.011) (0.011) (0.010) (0.020) (0.023) (0.005) hsize 0.0386 0.2105*** 0.2254*** 0.1020 0.0947 0.0776** (0.052) (0.050) (0.056) (0.108) (0.155) (0.032) hearn 0.0225 0.0437 0.0737** 0.1291** 0.0227 0.1015*** (0.029) (0.031) (0.033) (0.058) (0.092) (0.021) hregion 0.1808*** 0.1897*** 0.1059*** 0.0709 0.1187*** 0.1098*** (0.027) (0.022) (0.026) (0.045) (0.046) (0.013) hhmale 0.0482** 0.0224 0.0289 0.0287 0.1124* 0.0203* (0.021) (0.020) (0.020) (0.035) (0.068) (0.012) hhmarried 0.0235 0.0211 0.0275 0.0602 0.1615** 0.0033** (0.027) (0.025) (0.020) (0.039) (0.081) (0.013) hhedu 0.0387* 0.1372*** 0.0595 0.0350 0.1486 0.0456*** (0.023) (0.027) (0.037) (0.068) (0.125) (0.016) hhexp 0.1128** 0.0895** 0.1186** 0.0927 0.1275 0.0419 (0.049) (0.042) (0.050) (0.106) (0.207) (0.027) hhind 0.0669 0.0756* 0.0859* 0.1171** 0.0162 0.0865*** (0.061) (0.043) (0.047) (0.051) (0.080) (0.024) const 0.2033** 0.1426* 0.1153 0.1882 0.0365 0.1346*** (0.089) (0.077) (0.083) (0.136) (0.288) (0.041) other 0.0029 0.0338*** 0.0254** 0.0312** 0.0093 0.0110* (0.007) (0.008) (0.010) (0.015) (0.022) (0.006) Note: ***, **, * = significant at the 1%, 5%, and 10% levels, respectively. expenditure distribution. Household size and number of earners in the household are important in accounting for the change around the middle of the expenditure distribution; thus, coefficient changes associated with household size have contributed towards increasing urban rural inequality by approximately 32 percent, while coefficient changes associated with the number of earners have contributed towards decreasing inequality by 24 percent. At the bottom of the distribution, both education and industry affiliation contributed towards increasing urban rural inequality; specifically, changes in the urban rural gap in returns to education have increased the overall inequality by 8.5 percent at the 10th percentile and 25.2 percent at the 25th percentile. Changes in returns associated with industry affiliation had an inequality increasing effect of 37.8 percent at the 10th percentile and 16.5 percent at the 25th percentile. Finally, the last row (component V, referred to as other ) shows the extent to which the coefficients effect in year 1990 would be reduced if it were measured using characteristics of urban residents in 2006. The double decomposition results for inequality changes (Table 6) are subject to a similar discussion as those of the single decompositions (Tables 3 5). Summarizing, urban rural inequality in Thailand declined after the late 1990s, both on average and at every point of the expenditure distribution, except at the
ASIAN ECONOMIC JOURNAL 302 bottom of the expenditure distribution. The change in inequality in living standards over time is decomposed into two main components: one is due to changes in urban rural differences in household characteristics and the other due to changes in urban rural differences in the effect of those characteristics on percapita expenditure. Within the first component, changes in urban rural educational attainment constitute the most influential factor, while changes in region and household size are also important. Within the second component, changes in urban rural effects of region of residence explain a substantial share of the inequality convergence across the whole distribution, with the exception of the 90th percentile. The contribution of changes in the coefficients of other covariates varies and is of secondary importance. Looking forward, the findings on the main contributors to the narrowing of the urban rural gap in Thailand (these being narrowing of the education gap and demographic changes) generate certain expectations about future developments in urban rural inequality in Thailand and identify at least one possible target for policy intervention. The trend over time for smaller families and fewer children per family as well as increases in labor force participation in rural areas contribute towards the narrowing of the urban rural gap in living standards; such demographic changes are expected to continue to contribute towards narrowing intersectoral inequality. The other major contributor, narrowing of the education gap, could be the target of policy intervention. Policy-makers could intensify efforts to increase education acquisition in rural areas, especially in poor communities. Further narrowing of the education gap between urban and rural areas would be instrumental in reducing inter-sectoral inequality. VI. Conclusions Thailand is probably an exception within Asia in that urban rural inequality in living standards (as measured by the per-capita household consumption expenditure) has declined continuously since the early 1990s. In 1990, on average, the per-capita consumption of urban households was approximately 77 percent higher compared to rural households. By 2006, the gap had narrowed, with urban household expenditure approximately 61 percent higher compared to rural household expenditure. Over the entire period, the urban rural gap was wider at higher points of the expenditure distribution. In the present paper, we addressed questions such as to what extent urban rural living standards converged over time and the extent of heterogeneity in convergence across the expenditure distribution In addition, to what extent have changes in household characteristics in urban and rural areas over time been responsible for the narrowing of the gap in comparison to changes in the coefficients associated with these characteristics? What are the most important individual household characteristics associated with the narrowing of the gap in living standards? We find that the urban rural gap in living standards in Thailand has narrowed substantially since the early 1990s, both at the mean and especially for households at the upper part of the expenditure distribution; however, the narrowing of the